Autonomous data machines and systems

ABSTRACT

Autonomous data machines and systems may be provided, which may be deployed in an environment. The machines may roam within the environment and collect data with aid of one or more sensors. The data may be sent to a control center, which may optionally receive information from additional data sources, such as other on-site sensors, existing static data, or real-time social data. The control center may send instructions to the machines to perform one or more reaction based on the received information. The autonomous data machines may be capable of reacting autonomously to one or more detected condition. In some instances, the autonomous data machines may be employed for security or surveillance.

CROSS-REFERENCE

This application is a continuation application which claims the benefitof U.S. application Ser. No. 14/751,115, filed Jun. 25, 2015, whichclaims the benefit of U.S. application Ser. No. 14/599,073, filed onJan. 16, 2015, which claims the benefit of U.S. Provisional ApplicationNo. 61/929,007, filed Jan. 17, 2014, each of which is incorporatedherein by reference.

BACKGROUND OF THE INVENTION

Crime is a significant problem that has a large detrimental effect, bothpersonally and economically. For instance, crime has a $1+trillionnegative economic impact on the United States economy. Violent crime andproperty crime occur with high frequency. The security market usessecurity guards, law enforcement personnel, and law enforcement vehiclesto aid in combating crime. Existing security systems, however, may becostly to implement, due to the large number of security personnelneeded for effective surveillance. Furthermore, crime still remains avery significant problem with a great deal of associated cost.

SUMMARY OF THE INVENTION

A need exists for improved systems, methods, and devices for combatingcrime. The present invention provides systems, methods, and devices thatmay assist with predicting and preventing crime. Autonomous datamachines and systems can be used for surveillance and security purposes,and thereby reduce the negative personal and economic impact of crime.Autonomous data machines may be provided in a large-scale deployment ofautonomous technology, sensors, robotics, big data, and predictiveanalytics, may gather real-time on-site data and combine it withexisting large data sets as well as relevant geo-fenced social networkfeeds, to allow for an ability to provide predictive mapping in a givenenvironment. These self-driving robots may help society build safer,engaged communities while significantly reducing costs and crime, andsaving lives.

In some implementations, autonomous data machines may freely roam anenvironment and collect information that can be used to detect orprevent crime. The autonomous data machines may communicate withexternal systems and devices, and utilize information from othersources, such as a-priori information. Predictive analysis may be usedon the data collected by the autonomous data machines and additionaldata sources. Alerts may be provided based on risk factors and machinelearning. The autonomous data machines may also communicate with thirdparties, such as security systems or individuals. Efficient deploymentof resources may be provided on-site based on the analyzed data.

Additional aspects and advantages of the present disclosure will becomereadily apparent to those skilled in this art from the followingdetailed description, wherein only illustrative embodiments of thepresent disclosure are shown and described. As will be realized, thepresent disclosure is capable of other and different embodiments, andits several details are capable of modifications in various obviousrespects, all without departing from the disclosure. Accordingly, thedrawings and description are to be regarded as illustrative in nature,and not as restrictive.

INCORPORATION BY REFERENCE

All publications, patents, and patent applications mentioned in thisspecification are herein incorporated by reference to the same extent asif each individual publication, patent, or patent application wasspecifically and individually indicated to be incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity inthe appended claims. A better understanding of the features andadvantages of the present invention will be obtained by reference to thefollowing detailed description that sets forth illustrative embodiments,in which the principles of the invention are utilized, and theaccompanying drawings of which:

FIG. 1A shows an example of an autonomous data machine in accordancewith an embodiment of the invention.

FIG. 1B shows a view of the autonomous data machine where a portion ofthe housing is opened to expose the interior.

FIG. 2 shows a view of the autonomous data machine with an internalstructure.

FIG. 3 shows an example of an autonomous data machine having analternative form factor.

FIG. 4A shows an example of an autonomous data machine with theunderlying support structure as seen through the housing.

FIG. 4B shows a close-up view of a lower portion of the autonomous datamachine.

FIG. 4C shows an example of a sensor arrangement in accordance with anembodiment of the invention.

FIG. 5 shows an example of LIDAR which may be provided on the autonomousdata machine.

FIG. 6A provides an example of license plate recognition using one ormore vision sensor of the autonomous data machines.

FIG. 6B shows an example of progress mapping at a construction site.

FIG. 6C shows an example of traffic that may be analyzed with aid of anautonomous data machine.

FIG. 6D illustrates how autonomous data machines may be useful fordetection and tracking of individual objects.

FIG. 7 provides an example of a robotic machine operating systemimplementation.

FIG. 8 shows an example of a modular deployment of autonomous datamachines.

FIG. 9A shows an example of a plurality of autonomous data machinescommunicating with a control center.

FIG. 9B shows a plurality of autonomous data machines deployed withindifferent geofences in communication with the control center.

FIG. 10 shows an example of different communication channels that may beused.

FIG. 11 shows an example of data analytics in accordance with anembodiment of the invention.

FIGS. 12A and 12B shows examples of inductive charging mechanisms inaccordance with an embodiment of the invention.

FIG. 13 provides an example of an autonomous data machine patrolling adesignated environment.

DETAILED DESCRIPTION OF THE INVENTION

The invention provides systems and methods for combating crime with theaid of autonomous data machines in accordance with aspects of theinvention. Various aspects of the invention described herein may beapplied to any of the particular applications set forth below or for anyother types of security or surveillance systems. The invention may beapplied as a standalone system or method, or as part of an event-drivendata collection and reaction system. It shall be understood thatdifferent aspects of the invention can be appreciated individually,collectively, or in combination with each other.

Autonomous data machines may be provided in an environment and mayperform one or more missions in the environment. For example autonomousdata machines may be deployed at a location and used for security orsurveillance at the location. Autonomous data machines may be robotsthat can freely roam the environment at which they are deployed. Theymay collect information and react in response to the collectedinformation. In some instances, they may communicate some or all of thecollected information to the control center. The control center mayanalyze the data, optionally in conjunction with data from othersources. In some instances, the control center may send informationand/or instructions to the autonomous data machine. The autonomous datamachine can also interact with humans or other external systems, such asa local security or law enforcement system.

Autonomous Data Machine

An autonomous data system may have multiple technology layers. Forexample, a robot control layer may be provided, which may assist withcontrol of one or more autonomous data machines in the system. The robotcontrol layer may include autonomous and/or semi-autonomous activity ofthe autonomous data machines. Functions such as machine learning,propulsion, charging, stopping, turning, sleep, and/ormachine-to-machine (M2M) interaction may be included in the robotcontrol layer. A sensor layer may be provided. The sensor layer may becapable of performing audio and/or visual sensing. The sensor layer maybe capable of performing optical character recognition, omnidirectionalimaging, thermal imaging, sound collection through microphones,ultrasonic sensing, infrared sensing, lidar, air quality assessment,weather and other environmental sensing including barometric pressure,temperature, humidity, pollution, smoke, CO2, noise, sensing ofradiation, chemical and biological agents or hazards, sensing ofelectromagnetic radiation, sensing presence and identity of radio,cellular or wireless devices, etc. The system may also include a dataanalytics and system control layer. This may include a graphical userinterface, remote operation, real time review, decision support and/ormission planning. In some implementations, the data analytics may occuron-board a robot, at a control center external to the robot, or anycombination thereof.

FIG. 1A shows an example of an autonomous data machine in accordancewith an embodiment of the invention. The autonomous data machine may bea self-propelled surveillance or data collection robot. The autonomousdata machine may have one or more propulsion units 110, a power supply,a housing 120, one or more sensors 130, communication unit, controller,memory, and/or interface unit.

The autonomous data machine may be capable of freely roaming about anenvironment. The machine may freely move along three dimensions or twodimensions. The machine may freely move over a surface or may besemi-restricted to certain areas or types of surfaces. The data machinemay freely roam over gravel, pavement, grass, sand, dirt, carpet,concrete floors, hardwood floors, or any other surface. The autonomousdata machine may be able to traverse transitions between different typesof surfaces. The autonomous data machines may or may not changepropulsion units or methods when transitioning to different surfacetypes. For example, the autonomous data machine may move about with theaid of one or more propulsion unit, which may include wheels 110,rollers, legs, arms, propellers, rotors, or movable body surfaces. Themachine may be self-propelled. The machine may be capable of moving onits own without requiring the aid of a human or other live being.

In some embodiments a propulsion unit may include a plurality of wheelsthat may permit the autonomous data machine to roll over an underlyingsurface. In some examples, two, three or four wheels may be providedwhich may permit the autonomous data machine to stand stably while notmoving. In some instances, stabilization may occur with aid of one ormore wheels or other stabilization platforms, such as gyroscopicplatforms. The wheels may vary in size or be the same size. In somecases, the wheels can have a diameter of at least about 1 cm, 2 cm, 3cm, 4 cm, 5 cm, 8 cm, 9 cm, 10 cm, 15 cm, 20 cm, 25 cm, 30 cm, 35 cm, 40cm, 45 cm, 50 cm, 55 cm, 60 cm, 65 cm, 70 cm, 75 cm, 80 cm, 85 cm, 90cm, 95 cm, 100 cm, 150 cm, or 200 cm. The wheels can have a smooth ortreaded surface. The wheels may also permit the autonomous data machineto move laterally and/or rotate in place. The autonomous data machinemay be capable of making any combination of translational or rotationalmovement.

The propulsion unit may be driven with aid of one or more drive units.For example, a motor, engine, drive train, or any other component may beprovided that may aid in driving the propulsion of the autonomous datamachine. In some instances a drive unit may be proximally located to thepropulsion units to drive the propulsion units. Alternatively they maybe operably linked without necessarily being adjacent or in closeproximity to one another.

One or more power supplies may be used to power the autonomous datamachine. A power supply may be an energy storage device, such as one ormore batteries. The batteries may be rechargeable batteries (i.e.secondary batteries). Batteries having any battery chemistry known orlater developed in the art may be used. In some instances, batteries maybe lead acid batteries, valve regulated lead acid batteries (e.g., gelbatteries, absorbed glass mat batteries), nickel-cadmium (NiCd)batteries, nickel-zinc (NiZn) batteries, nickel metal hydride (NiMH)batteries, or lithium-ion (Li-ion) batteries. The one or more powersupplies may power one or more components of the autonomous datamachine. The power supplies may be used to power propulsion of theautonomous data machine, such as a motor that mean effect turning of oneor more wheels of the autonomous data machine. The power supplies maypower any other components of the autonomous data machine, such as oneor more sensors 130, communication unit, controller, memory, and/ordisplay/audio unit. The same power supply may be used for multiplecomponents, or different power supplies may be used for differentcomponents. Batteries may also be exchanged or swapped out. A controllerof the machine may register state of charge of the batteries and mayprovide an instruction for the machine to recharge. The machine canautonomously approach and connect to a recharging station based on aninstruction from the controller when the state of charge of the batteryis below a predetermined threshold. The controller may also detectwhether the machine should enter a power savings mode and/or limitcommunication.

The autonomous data machine may have one or more housing 120. In someembodiments, the housing may be a solid shell that may protect one ormore components of the autonomous data machine, such as a sensor payloadand robotic platform. The housing may contain one or more components ofthe autonomous data machine. The housing may completely or partiallyenclose one or more components of the autonomous data machine. Forexample, the drive unit may be contained within the housing. One or morecomponents of the autonomous data may be provided external to thehousing, attached to an exterior of the housing, or completely orpartially embedded in the housing. In some examples, a propulsion unitmay be partially or completely exposed outside the housing. For example,wheels may protrude from the bottom of the housing to contact theground. Optionally, one or more sensors may be partially or completelyexposed outside the housing, or may be completely enclosed within thehousing.

FIG. 1B shows a view of the autonomous data machine where a portion ofthe housing 120 is opened to expose the interior 124. In some instances,one or more objects or components of the autonomous data machines may bestored in the interior. For example, a sensor payload may be providedtherein. Alternatively, other autonomous data machines may be provided.Any sort of instrumentation, propulsion or drive unit, power unit, orother components may be provided therein. One or more panels of thehousing may be removable to provide a user with access to the interior.A user may access the interior to perform maintenance or repairs. A usermay access the interior to swap out one and/or exchange or morecomponents. A user may access the interior to swap out and/or exchangeone or more batteries. In some instances, a user may access any type ofpayload therein.

FIG. 2 shows a view of the autonomous data machine with an internalstructure 140. The internal support structure may include one or moresupport bars or support platforms 142, 144. In some instances, one ormore sensors 130 or other components may be supported by the internalstructure. For example, one or more sensors may be provided on a supportplatform of the internal structure. In some embodiments, multiple layersof lateral support platforms may be provided to support multiple layersof sensors or other components.

FIG. 4A shows an example of an autonomous data machine with theunderlying support structure as seen through the housing. The supportstructure may comprise brackets, plates, and/or rods arranged to providestructural support for one or more components. The support structure canbe made from metallic, composite, and/or polymer material. As previouslydescribed, various components, such as the propulsion/drive units, powersupplies, sensors, communication unit, controller, memory, and/orinterface unit may be provided therein. The machine may have asubstantially circular cross-section. Sensors may be distributed aroundthe body to provide the machine with omni-directional sensing. Themachine's sensing capabilities may be substantially the same from anyangle around the machine.

FIG. 4B shows a close-up view of a lower portion of the autonomous datamachine. One or more wheels may be used to aid in propelling theautonomous data machine. Underlying support structure may aid insupporting the housing, sensors, and/or other components therein. Acontroller and/or power supply may be provided, and optionally carriedon the support structure.

The autonomous data machine may have any size, shape, or form factor. Insome examples, the autonomous data machine may have one or moredimensions (e.g., width, length, height, diagonal, and/or diameter) thatmay be greater than or equal to about 1 foot, 2 feet, 3 feet, 4 feet, 5feet, 6 feet, or 7 feet. Optionally, an autonomous data machine may haveone or more dimensions (e.g., width, length, height, diagonal, and/ordiameter) that may be less than or equal to about 2 feet, 3 feet, 4feet, 5 feet, 6 feet, 7 feet, or 10 feet. The machine may be taller thanit is wide, or vice versa. The autonomous data machine may weigh morethan or equal to about 20 pounds, 50 pounds, 100 pounds, 150 pounds, 200pounds, 250 pounds, 300 pounds, or 350 pounds. Optionally, theautonomous data machine may weigh less than or equal to about 100pounds, 150 pounds, 200 pounds 250 pounds, 300 pounds, 350 pounds, 400pounds, or 500 pounds. The size, shape, and/or form factor may bedetermined by the housing 120 size, shape, and/or form factor. Theautonomous data machine may be sized or weighted to be too large orheavy for an individual to easily destroy, pick up, take, tip over, ortake other actions. Alternatively, the autonomous data machine may be ofa size and/or weight to be lifted or carried by an individual.

The autonomous data machine may be shaped with a curved surface. Thehousing of the data surface may form a substantially smooth curvedsurface. The machine may have a round cross-sectional shape. In someinstances, the cross-sectional shape may be circular or elliptical.Having a rounded shape with sensors around may permit the machine tosense in 360 degrees around the machine. The machine vision sensors maybe able to capture images from all around the machine simultaneously.Regardless of which way the machine is oriented, data such as images maybe collected from any angle around the machine. The machine may besubstantially bullet shaped with a rounded top. In some instances, thebottom portion of the machine may be heavier than the top portion of themachine. The machine may have a low center of gravity to aid withstability. Optionally, the bottom portion of the machine may have alarger cross-sectional area than the top portion of the machine. Thebottom portion of the machine may be wider than the top portion, therebyaiding with stability. In some instances, the sensors may be distributedat any height of the machine. The sensors may be distributed high enoughto gain a wide field of view. For instance, image capture sensors may beseveral feet (e.g., about 2 feet, 3 feet, 4 feet, or 5 feet) above thesurface of the ground.

FIG. 3 shows an example of an autonomous data machine having analternative form factor. In some examples, the autonomous data machinesmay be an upright small vehicle (e.g., FIG. 1A). The upright smallvehicles may use a two-wheel platform with tilt stabilization (which maybe gyroscopic or use a third and/or fourth wheel). In someimplementations the autonomous data machines may have a large vehicleshape (e.g., FIG. 3). A three-wheel or four-wheel platform may be used.Optionally, skid steering may be used for simplicity. In somealternative implementations, the low-lying vehicle shape may be sized tobe a small or large vehicle. A large vehicle implementation may be ofcomparable size to a vehicle used to carry a human from one location toanother.

The autonomous data machine may have one or more sensors 130. Some ofthe sensors may be data collection sensors that may collect data to beanalyzed on-board the autonomous data machine or to be sent to a controlcenter, where it may be analyzed further. Some of the sensors may benavigation sensors, which may assist the autonomous data machine withmoving autonomously or semi-autonomously about its environment. Somesensors may function as both data collection sensors and navigationsensors (e.g., data collected from the sensors may be analyzed furtherin the context of the autonomous data machine's mission—e.g.,surveillance, security, etc., and may also be used to assist theautonomous data machine in navigating and interacting with theenvironment). Some of the sensors may collect data for the machine'smission while some sensors may be reflective of a state of the machine,or both.

Some examples of sensors that may be employed by the autonomous datamachine may include remote sensors, such as laser imaging detection andranging (LIDAR) 130 a, radar, sonar; a sensor array 130 b; differentialglobal positioning system (DGPS) 130 c; inertial measurement unit (IMU)which may include one or more gyroscopes, magnetometers, oraccelerometers; ultrasonic sensors 130 d; image sensors (e.g., visiblelight, infrared), heat sensors, audio sensors, vibration sensors,conductivity sensors, chemical sensors, biological sensors, radiationsensors, conductivity sensors, proximity sensors or any other type ofsensors. The sensors may be distributed within the autonomous datamachine housing, on the data machine housing, and/or as part of the datamachine housing.

The DGPS sensor may be used to assist the autonomous data machine innavigating its environment. Any description herein of a DGPS sensor mayapply to any type of GPS sensor. The DGPS sensor can communicate withone or more ground based reference station and/or GPS satellites toobtain one or more GPS data signals. The DGPS system may preferably usea network of fixed, ground-based reference stations to broadcast thedifference between the positions indicated by the GPS satellite systemsand the known fixed positions. The stations may broadcast the differencebetween the measured satellite pseudoranges and actual (internallycomputed) pseudoranges, and receiver stations may correct theirpseudoranges by the same amount. The DGPS sensor may be providedexterior to or within a housing of the autonomous data machine. Thesignals received by the DGPS sensor can be processed to determine theposition of the autonomous data machine relative to a global referenceframe (e.g., latitude, longitude, and altitude), and thereby determinethe translational velocity and/or acceleration of the autonomous datamachine. The DGPS sensor can utilize any suitable GPS technology, suchas differential GPS (DGPS) or real time kinematic (RTK) GPS. The GPSsensor can be configured to determine the position of the autonomousdata machine to any suitable level of accuracy, such as meter-levelaccuracy (e.g., within 10 m, 5 m, 2 m, or 1 m of accuracy) orcentimeter-level accuracy (within 500 cm, 200 cm, 100 cm, 50 cm, 20 cm,10 cm, or 5 cm of accuracy).

In some instances, an IMU may also aid in the navigation of theautonomous data machine. An IMU can include one or more accelerometers,one or more gyroscopes, one or more magnetometers, or suitablecombinations thereof. For example, the IMU can include up to threeorthogonal accelerometers to measure linear acceleration of the movableobject along up to three axes of translation, and up to three orthogonalgyroscopes to measure the angular acceleration about up to three axes ofrotation. The IMU may be provided exterior to or within a housing of theautonomous data machine. The IMU can provide a signal indicative of themotion of the autonomous data machine, such as a position, orientation,velocity, and/or acceleration of the aerial vehicle (e.g., with respectto one, two, or three axes of translation, and/or one, two, or threeaxes of rotation). For example, the IMU can sense a signalrepresentative of the acceleration of the autonomous data machine, andthe signal can be integrated once to provide velocity information, andtwice to provide location and/or orientation information. The IMU mayprovide a signal to a controller of the autonomous data machine.

In some instances, a combination of the GPS and IMU may be used to aidin the navigation and/or movement of the autonomous data machine in itsenvironment. Alternatively, such sensing systems may operateindependently and/or for different purposes.

One or more sensors may be provided in a sensor array 130 b. The sensorarray may include a plurality of different sensors or types of sensors.The sensor array may include data collection sensors that may collectinformation that may be analyzed in the context of the mission of theautonomous data machine. The sensors in the sensor array may be providedas one or more rows or one or more columns of sensors. The sensors mayencircle a support structure, e.g., a rounded support structure, orsupport structure of any shape. In some instances, the sensor array maybe provided on exterior surface of a housing of the autonomous datamachine. Alternatively, the sensor array may be provided within ahousing of the autonomous data machine. In some instances, a transparenthousing 122 or portion of the housing may be provided around the sensorarray. The transparent portion of the housing may permit certain sensorsto collect information through the housing. For example, images may becaptured through a transparent portion of the housing. In anotherexample, a thermal scan may occur through a portion of the housing. Thetransparent housing may protect the sensor array therein.

FIG. 4C shows an example of a sensor arrangement in accordance with anembodiment of the invention. The autonomous data machine may be capableof performing multiple functions using multiple ‘senses’. The autonomousdata machine may be configured to see, hear, feel, and/or smell. Theautonomous data machine may be able to provide one or more, two or more,three or more, or all four of these functions.

The autonomous data machine may be able to see using one or more imagingsensors. In some instances, nighttime and daytime 360 degree imaging maybe provided. In one example, 360 degree imaging may be provided with aidof multiple image capture devices located around a circular base (e.g.,in the sensor array). The image capture devices may be configured tocapture static images (e.g., snapshots) or dynamic images (e.g., video).The image capture devices can simultaneously capture static images(e.g., snapshots) or dynamic images (e.g., video). Multiple images frommultiple image capture devices may be stitched together to form a smoothuninterrupted image (e.g., 90 degrees, 180 degrees, 270 degrees, 360degrees). The image capture devices may be any type of camera, such as aPanasonic WV-SP306 or a camera that shares one or more characteristicswith the Panasonic WV-SP306 or other cameras. The camera may collectimages in high definition (HD). In some instances, the camera maycollect images in color or black and white. The cameras may collectimages at a fast frame rate (e.g., 25 Hz or higher, 50 Hz or higher, 75Hz or higher, 100 Hz or higher). Cameras may capture images at a videorate. Cameras may optionally have high-sensitivity daytime/nighttimeimaging capabilities with adaptive dynamic range. Compression, such asH.264 dual bandwidth video bandwidth compression may occur to permitlow-bandwidth streaming and high-bandwidth recording. The cameras mayoptionally have an IP-based data connection. Individual communicationand control of each of the cameras may be provided. In some instances,the cameras may be integrated in an automated face detection system.Facial recognition may be performed on images captured by the cameras.The camera can be in communication with a facial data base to determinethe identity of an individual using the facial recognition. In somecases, the camera can be used to scan a group of two or more people toidentify a specified person using facial recognition. Other types ofoptical analysis and recognition may be employed locally at the machineor remotely at a control center. A wide field image may be captured by avisible light camera. Images (e.g., snapshots or video) may be streamedto an external device such as a control center server in real-time.Alternatively, periodic or event-driven transmission may occur. In anexample, the data machine can transmit images when the data machinedetects abuse by an individual. The data machine can transmit imageswhen the data machine detects a human or object in the environment. Thedata machine can transmit images when the data machine detects aninteraction between two or more individuals.

FIG. 5 shows an example of LIDAR which may aid the autonomous datamachine in being able to ‘see’ or perform visual detection. One or morelaser beams (e.g., 600-1500 nm) may be scanned around in two dimensionsto illuminate a surrounding environment. One or more sensor may gathermeasurements of distance and surface properties. A three-dimensionalscan may be generated by combining scanning lasers in multiple planes.In some instances, a 360 degree horizontal field of view may be possibleto implement with one sensor, or by combining multiple sensors. Multiplesensors may perform detection in different scanning planes anddirections, such as forward, backward, horizontal, vertical or downward.One or more three-dimensional distance scans may be provided from alaser scanner (LIDAR).

The autonomous data machine can detect and identify letter, number,and/or other symbols. In some embodiments, automated license platerecognition (ALPR) sensors may be used. The ALPR sensors that may beused may include those from Vigilant Solutions, or share anycharacteristics with those used in Vigilant Solutions. ALPR may usevisual imaging to aid in the license plate recognition. Such recognitionmay occur on-board the autonomous data machine, or may occur offboard ata control center or another location.

In some instances, optical character recognition may be provided. Forexample, the autonomous data machine may be able to ‘read’ lettersand/or numbers. In some instances, facial recognition, expressionrecognition, or gesture recognition functions may be provided. In oneexample, an image capture of a face may be scanned and compared withother known faces in a database to determine if there is a match. One ormore mapping function may also be provided. For example, 2D or 3Dmapping may be provided based on images collected. Examples of audioanalytics are provided elsewhere herein.

The autonomous data machine may be able to hear using one or more audiosensors, such as microphones. In some instances, directional audio maybe used. Ambient noise may also be collected and/or analyzed. In someinstances, ambient noise may be discernible from other noises ofinterest. The autonomous data machine cans subtract ambient noise whencollecting audio to reduce unnecessary audio detections. In some cases,the autonomous data machine can detect unexpected changes in the ambientnoise. For example, if an environment has a machine that is expected tooperate and generate noise continuously the autonomous data machine maydetect the absence of the machine operating noise. The autonomous datamachine can determine that absence of the machine operating noise mayindicate that the machine has shut down or failed unexpectedly. Theautonomous data machine can make a call for service for the machine.

Additionally, the autonomous data machine may be able to perform one ormore feeling functions. For example, thermal imaging and/or temperaturesensing may occur for the autonomous machine to collect data about itsenvironment. An infrared (IR) camera may be used to perform thermalimaging. IR cameras may be able to perform detection of heat emittingobjects, such as human objects. The autonomous data machine candetermine when a human has an unexpectedly low or high body temperature.Any IR camera known or later developed in the art may be used. Thermalimages may be obtained in partial or complete darkness from an IRcamera. Similarly, one or more proximity sensor may be employed. Theproximity sensor may detect one or more obstructions, objects,structures, or living beings near the autonomous data machine. In someexamples, an ultrasonic distance sensor 130 d may be used to measureproximity to various objects. The ultrasonic distance sensor mayfunction as a failsafe mechanism to capture objects that other sensors(e.g., ‘seeing’ sensors such as LIDAR and camera sensors) may not havecaptured to avoid collisions. In some instances, the ultrasonic distancesensors may have a range of about 15-30 cm. The ultrasonic distancesensors may be placed on and around the body of the autonomous datamachine. For instance, 6-10 sensors may be provided around theautonomous data machine. They may encircle the autonomous data machine.Ultrasonic sensors as known or later developed in the art may be used.

A smell function may also be provided for the autonomous data machine.The smell function can comprise detecting one or more air bornemolecules in the proximity of the data machine. For example, theautonomous data machine may be able to detect one or more chemicals,radiation, biological agents, and/or pathogens. Chemical analysis of asubstance may occur and comparison may be made with information in adatabase. For example, databases of characteristics of substances may bedownloaded to the machine or accessible by the machine. Such databasesmay be updated. Such substances may be detected through the ambientenvironment (e.g., air, water, soil, surface), or may be detectedthrough one or more collected samples, such as air/gas samples, watersamples, soil samples, biological samples, or other collected samples.The machine may self-collect samples or receive a sample provided by auser. The machine may be capable of detecting harmful environmentalagents.

The autonomous data machine may also include a communication unit. Thecommunication unit may optionally be provided partially or completelywithin a housing 120 of the autonomous data machine. The communicationunit may provide communication between the autonomous data unit and acontrol center. The communication unit may also provide communicationbetween autonomous data machines. In some instances, communication mayoccur between the autonomous data unit and third party devices, such asa security system of an environment being patrolled by the autonomousdata machines, or mobile devices of individuals within the environmentor related to the environment. Any communications provided herein may betwo-way communications. Alternatively, some one-way communications maybe provided (e.g., only from the autonomous data machine to an externaldevice, or from an external device to the autonomous data machine).

Any of the communications provided herein may occur directly.Alternatively, they may occur over a network, such as a local areanetwork (LAN) or wide area network (WAN) such as the Internet.Communication units may utilize LANs, WANs, infrared, radio, WiFi,point-to-point (P2P) networks, telecommunication networks, cloudcommunication, and the like. Optionally, relay stations, such as towers,satellites, or mobile stations, can be used. Wireless communications canbe proximity dependent or proximity independent. In some embodiments,line-of-sight may or may not be required for communications. Furtherdescriptions of possible modes of communication are described in greaterdetail elsewhere herein.

One or more controllers may be provided for an autonomous data machine.The controllers may include one or more processors that may perform oneor more steps in accordance with non-transitory computer readable mediathat may define operation of the autonomous data machine. The processormay determine, based on data, how the autonomous data machine shouldoperate (e.g., move in its environment, collect data, communicate withother devices or systems, provide alerts, control display, interact withindividuals or its environment). The processor may make thisdetermination in accordance with data collected by the autonomous datamachine, received from the control center, and/or received from anyother source.

A controller may have one or more memory units that may includenon-transitory computer readable media that may comprise code, logic, orinstructions for performing the one or more steps. For example,transitory computer readable media having algorithms for analyzing astate of the machine may be provided on-board the autonomous datamachine and accessed by one or more processors of the controller.Algorithms for analyzing some of the collected data may also be providedon-board. The memory may store data collected by the sensors of themachine. In some instances, the data may be stored for a predeterminedperiod of time (e.g., several hours, a day, several days, a week,several weeks, a month, several months). The data may be stored untilthe machine receives an instruction to delete the data. The data may bestored prior to being communicated to an external device or system. Thedata may be stored even if the data has also been communicated to anexternal device or system. In some embodiments, data from external datasources may also be stored in memory. For example, a map of theenvironment may be provided from a public database. Data pertaining tothe map may be stored on-board the machine. In another example, datapertaining to social media in the area may be received by the machineand stored on-board the machine.

In some embodiments, a controller may use data collected by the machinein order to determine the state of the machine and/or determining thenext acts of the machine. The controller may also optionally includedata from external data sources, such as the control center, on-sitedata sources, static data sources, or social media data sources.

An operating system for a robotic machine may be used by the autonomousdata machine. The robotic machine operating system may optionallydictate the performance of one or more of the controllers. The operatingsystem may provide libraries and tools to help software developerscreate robot applications. It may provide hardware abstraction, devicedrivers, libraries, visualizers, message-passing, package management,and/or additional functionality. In some instances, the availability ofa robotic machine operating system may enable a rapid commercializationof robotic technology.

FIG. 7 provides an example of an implementation of a robotic machineoperating system. The operating system may provide the core functionsfor the autonomous data machine. This may include anything fromcollision detection, path finding, or calculation of linear/turn speeds.Application specific interfaces to the operating system may be developedand used for the autonomous data machines.

As shown inputs may be provided to a robot motion and navigationcontroller (e.g., move base). This may include info from a map server,one or more sensor sources, one or more odometry sources, sensortransforms (i.e., which may transform sensor signals from the sensorsources to a sensor coordinate system to a global robot coordinatesystem), and/or adaptive Monte Carlo localization (amcl). Suchinformation may go to a global planner, global costmap, local costmap,local planner, and/or recovery behaviors as identified. The robot motionand navigation controller may provide data to a base controller toeffect movement of the autonomous data machine.

An autonomous data machine may have one or more interface units. Theinterface units may enable the autonomous data machine to communicatewith one or more individuals in its environment. For example, theinterface unit may have a visual component, such as a display. One ormore lights (e.g., LEDs, OLEDs), or screens may be provided throughwhich information may be displayed. For example, combinations of lightsflashing in a pattern may provide information. In another example, ascreen may show static words or images, or may show video images, suchas dynamically changing words and/or images. The interface unit may havean audio component. For example information such as speech, music, orother sounds may be emitted through an audio interface (e.g., speakers).The audio and visual components may or may not be coordinated. Theinterface units may permit the autonomous data machine to communicateinformation to the one or more individuals. In some instances, a usermay interact with the autonomous data machine and respond through theone or more interface units or other sensors. The machine may sense theuser reaction and base one or more future communications on the userreaction. For example, the machine may ask a user to state adestination, and may register the user's audible response. The machinemay use speech recognition to identify the user's stated destination,and then provide further information about the specific destination. Inanother example, when the autonomous data machine is being abused by ahuman the autonomous data machine can communicate a warning to theindividual to stop the abuse. In some cases, a human can abuse theautonomous data machine by attempting to deactivate the data machine,hitting and/or kicking the data machine, attempting to knock over thedata machine, intentionally blocking one or more sensors of the datamachine, or performing any other action that may inhibit one or morefunctions of the data machine.

The autonomous data machine may be capable of human-machine interaction.For example, one or more sensors of the autonomous data machine maycollect information about the human, and the information may be analyzedon-board the autonomous data machine or externally to interpret thehuman's communication. The data machine can determine an emotional stateof the human. The data machine can determine an emotional state of thehuman using data collected by one or more sensors. In an example theautonomous data machine can detect a smile, frown, tears, dilatedpupils, sweat, or other facial and/or body indicators of emotion. Theautonomous data machine can detect auditory indications of the emotionalstate of the human. For example, the autonomous data machine can detectlaughter, yelling, crying, screaming, or any other auditory indicatorsof an emotional state of a human.

For example, one or more microphones of the autonomous data machine maycollect audio information from the human. Speech recognition analysismay be employed to identify what the human is saying. For example, theautonomous data machine may respond to verbal commands or cues. In someinstances, the autonomous data machine may also be able to identifytones or volume of the human speech. Tones and/or volumes of humanspeech can indicate an emotional state of the human. For example, theautonomous data machine may be able to distinguish between normal speechand screaming or shouting. The autonomous data machine may be able toreact to determined emotional states of the human. For example, if thehuman is determined to be in distress, the autonomous data machine mayalert a third party, such as law enforcement, or any other partymentioned elsewhere herein.

One or more image capture devices of the autonomous data machine maycollect visual information about the human. Facial recognition programsmay aid in identifying the human. In some instances, facial expressionrecognition or gesture recognition analysis may occur. For example, theprogram may be able to distinguish between a smile and a frown, or mayidentify tears. In another example, certain arm or hand gestures may berecognized by the autonomous data machine. The autonomous data machinemay communicate such information to a control center, or other externaldevice. The autonomous data machine may or may not respond to therecognized facial expression and/or gesture. For example, the autonomousdata machine may recognize attention-getting gestures (e.g., waving ofarms), or other gestures (e.g., stop gesture). In another example ofgesture recognition, the autonomous data machine may be able torecognize sign language. The autonomous data machine may be able tointerpret the sign language and react accordingly, similar to as itwould for speech recognition. The autonomous data machine may be able toreact to the sign language, or any of the other gestures. The autonomousdata machine can detect and react to an interaction between two or morehumans. The autonomous data machine can determine if the interaction isa positive or negative interaction. Depending on a determination of apositive or negative interaction, the autonomous data machine may beable to take further action. For example, if a negative interaction istaking place, the autonomous data machine may alert a third party, suchas law enforcement, or any other party mentioned elsewhere herein. Theautonomous data machine may approach and/or issue a visual or audibleoutput when a negative interaction is taking place. For example, theautonomous data machine may query the individuals whether any assistanceis required.

The audio and/or visual information may be analyzed separately ortogether to determine a context and/or reaction of the autonomous datamachine. For example, the combination of yelling and waving arms maymore likely provide an alert condition than the combination of laughterand waving arms.

An autonomous data machine may communicate with humans in anenvironment, such as for greeting, response to an information request,display of status, display of crime risk, display of emergency messages,display of emotional or fun effects, or display of marketing messages.Some of the information may be automatically broadcast to humans. Forexample, during an emergency, such information may be announced by theautonomous data machine regardless of the actions of the humans aroundit. For example, a loudspeaker may make a blanket broadcast to anyone inthe area that a tornado is imminent and people should seek shelter. Someof the information may be provided to the humans in response to a verbalor visual cue from the human. For example, a verbal request from thehuman for information pertaining to a particular topic may cause theautonomous data machine to provide the information about the topic.

The autonomous data machine may use various media for communication withthe human. For example, the media may include audio, speech, images,light, motion, or movement. Any of the various interface units may beused to aid in the communication with the human. The autonomous datamachine may ‘speak’ using audio speakers. The messages may includepre-recorded messages or may utilize a natural language user interface.The autonomous data machine may ‘show’ information using images, light,or through motion or movement. For example, a user may ask theautonomous data machine to direct the user to a particular locationwithin the environment. Optionally, the autonomous data machine mayguide the user to the location by moving toward the location.

The interaction units may be placed anywhere on the autonomous datamachine housing. For example, one or more speakers may be positioned tobe near the head height of a human, and permit the human to hear thesound from the autonomous data machine clearly. In another example, theimage display units, such as screens, may be provided anywhere that maybe easily viewed by the human. For example, the image display units maybe roughly at eyesight level of a standing human, or anywhere else alongthe housing.

The exterior design of the autonomous data machine may include theplacement of displays, interaction elements, visual presence and/orbranding. In some instances, branding for the manufacturer and/ordistributer of the autonomous data machine may be visually discernibleon an exterior surface of the autonomous data machine. Optionally,branding of a company or organization that uses the autonomous datamachines may be provided on the exterior of the autonomous data machine.For example, autonomous data machine may be used to patrol the officesof Company A, and/or provide helpful information to visitors of CompanyA. The autonomous data machines may optionally include Company A'sbranding information.

The exterior design of the autonomous data machine may include a coatingthat may permit the exterior surface to be weather or abuse resistant.For example, a spray-paint-resistant coating may be used. This may makeit more challenging for a vandal to spray paint or alter the exteriorsurface

Human-machine interaction may also include a reaction by the autonomousdata machine to mitigate abuse. For example, if a human is attacking theautonomous data machine and/or attempting to destroy it, the autonomousdata machine may provide an audible alarm. The autonomous data machinemay also send an alert to an external device (e.g., security office ofCompany A), which may inform other parties that the autonomous datamachine is being attacked.

In some embodiments, an autonomous data machine may have a payload. Thepayload may be transported by the autonomous data machine. Optionally,the payload may be separable from the autonomous data machine.Alternatively, the payload may be affixed to the autonomous data machineor integral to the autonomous data machine. In some instances, a sensorpayload may be provided. In another example, a payload may be anotherautonomous data machine (of a same or different type).

FIG. 8 shows an example of a modular deployment of autonomous datamachines. An autonomous data machine 810 may be provided in anenvironment 800. The autonomous data machine may have a payload therein,which may include one or more other smaller autonomous data machines 820a, 820 b, 820 c. The autonomous data machines carried by the firstautonomous data machine may have any of the characteristics of theautonomous data machine as described elsewhere herein. In someinstances, the first autonomous data machine may carry the otherautonomous data machines within its housing. A panel may open up topermit the other autonomous data machines to exit the first autonomousdata machine.

The small autonomous data machines may be of the same type or may be ofdifferent types. In some examples, the small autonomous data machinesmay be configured to traverse the same type of environment or surface,or may be configured to traverse different types of environments orsurfaces.

For example, a first type of autonomous data machine 820 a may beconfigured to traverse a first type of environment/surface 800 a while asecond type of autonomous data machine 820 b may be configured totraverse a second type of environment/surface 800 b. A third type ofautonomous data machine 820 c may be configured to traverse a third typeof environment/surface 800 c. In one example, the first type ofenvironment/surface may be a solid ground surface, while the second typeof environment/surface may be water. In another example, the first typeof environment/surface may be pavement, while the second type ofenvironment/surface may be grass/mud/sand/dirt. In some instances, thefirst environment/surface may be finished or smooth, while the secondenvironment/surface may be rugged, muddy, or slippery. The third type ofenvironment may optionally be the air.

The various types of autonomous data machines may have differentpropulsion units or form factors that may permit them to traverse theirenvironment. For example, an autonomous data machine traversing a morerugged surface may have larger wheels with more pronounced treads. Inanother example, an autonomous data machine configured to fly throughthe air may have propellers or rotors instead of large wheels. In someembodiments, a payload of the autonomous data machine may be an unmannedaerial vehicle (UAV). The payload may optionally include a release andretrieval mechanism and/or charging set up. The UAV may be tethered tothe autonomous data machine or may be independent of the autonomous datamachine.

The mini-autonomous data machines may be deployed under certaincircumstances. For example, if the parent autonomous data machineencounters a type of surface or environment it can not traverse, it maydeploy the corresponding type of autonomous data machine that cantraverse it. In another example, if it encounters an event or conditionwhere a large amount of data needs to be collected rapidly, it maydeploy the mini-autonomous data machines. For example, the autonomousdata machine may be normally traversing an environment and may encountera car accident or explosion. It may deploy the other data machines tocover different angles of the situation and collect additional data.

The autonomous data machine and/or any of the smaller autonomous datamachines may be designed for robustness and ability to tolerate anenvironment. For example, water ingress protection may be provided. Thehousing may prevent water from entering the body of the autonomous datamachine and damaging the components therein. One or more sealingmechanism may be used to protect against water entering. In someinstances, elastomeric materials may be provided at or near edges ofpieces forming the housing. In some instances, a water repellent coatingmay be provided. Thus, the autonomous data machine may be able totolerate external weather conditions, such as rain, wind, or snow. Insome instances, the housing of the autonomous data machine may also havea protective coating to protect against the effects of sun.

In some embodiments, an autonomous data machine may have varioussystems, which may include a base vehicle system, electric system,vehicle motion control system, payload, communication, emergency, andcomputing environment, such as the systems described elsewhere herein. Abase vehicle system may include a chassis, suspension system, wheels,brakes, steering, exterior panels, glazing, thermal control, reductiongear, mechanical power, and a transmission. The electric system mayinclude an electric motor, HV power electronics, propulsion, batterypack, in-vehicle battery charger, wireless energy transmission to avehicle via a charge pad, vehicle energy watchdog, LV power electronics,payload, vehicle controller, lighting, communication, sleep function,wiring, and fuses. The vehicle motion control may include a lateralcontroller (e.g., speed), directional controller (e.g., steering),exception handler, docking to reference points, and docking to acharger. The payload may include sensors, such as a top mounted sensor,inside mounted sensors, UAV, and UGV. Communications systems may beprovided, including communications to infrastructure, cellular system,operator panel, UAV, and/or UGV. An emergency system may detectmechanical failure, electric failure, communication failure,accident/impact, when a machine is stuck, or when a machine isundergoing abuse. The computing environment may include hardware andsoftware.

Systems

One or more autonomous data machines may be part of an autonomous datasystem. FIG. 9A shows an example of a plurality of autonomous datamachines 910 a, 910 b, 910 c, 910 d communicating with a control center920. The autonomous data machines may be provided at the same locationor at different locations. For example, they may be within the sameenvironment or a different environment. The control center may be at asite remote to the autonomous data machines. For example, the controlcenter may be at a structure that is outside the area that theautonomous data machines roam. Alternatively, the autonomous datamachines may be able to roam the same structure that may be housing thecontrol center.

The control center may receive information from the autonomous datamachines and/or provide data (which may include instructions) to theautonomous data machines. The control center may include one or moreprocessors and/or memory units that may permit the control center toanalyze data and generate instructions. One or more displays may beprovided, through which control center administrators may view the dataand/or interact with the system.

FIG. 9B shows a plurality of autonomous data machines 910 e, 910 f, 910g, 910 h, 910 i deployed within different geofences 930 a, 930 b andcommunicating with the control center 920. The geofences may includevirtual perimeters of real-world geographic areas. The geofence could bedynamically generated (e.g., as in a radius around a structure or pointlocation). Or a geofence can be a predefined set of boundaries, likeschool attendance zones or neighborhood boundaries. Custom-digitizedgeofences may be used. The geofence boundaries may or may not overlapone another. Areas enclosed in the geofences may or may not overlap oneanother. The autonomous data machines may be provided within the samegeofence or may be deployed within different geofences. In someembodiments, each geofence may be defined by a user of the autonomousdata machines. For example, Company A may be a user of the autonomousdata machines to monitor Company A's site. Company A may define theboundaries of the geofence within which the autonomous data machines mayroam.

The autonomous data machines may all be the same type of autonomous datamachines or may include different types of autonomous data machines.These may include autonomous data machines of different form factors orhaving the ability to traverse different environments. The variousautonomous data machines may also include small autonomous data machinesthat may be deployed as payloads of other autonomous data machines.

A pre-defined location or area may be provided for an autonomous datamachine. The machine may move within the predefined area or location.Optionally, a geofence may mark the boundaries of the area or locationwithin which the autonomous data machine may roam. In some instances,the environment may be an outdoor environment. In other examples, indoorenvironments may be provided. A machine may be able to freely traversewithin the indoor and/or outdoor environment. Movement may be based ona-priori knowledge (e.g., maps and waypoints), sensors on the machines(e.g., LIDAR, camera, infrared, proximity, motion), support signals(e.g., assisted GPS beacon, color patterns, RFID) and autonomous datamachine algorithms. The autonomous data machines may be a standaloneunit or a swarm of robots. The a-priori knowledge may be communicatedfrom the control center, or from any other external device. For example,the machines may independently pull public information, such as mapinformation, from the Internet or any other data source. In someinstances, the machines may access information provided by a user of themachine (e.g., Company A that is deploying the machines may have maps ofCompany A's property with detailed features such as paved paths,structures, etc.). The machines may be able to learn the machines'environment. For example, as they encounter obstructions they may createa map of the obstructions or other features. When multiple machines aprovided within an environment they may share this information with oneanother and thus be able to more rapidly learn about features of theenvironment (e.g., obstructions, paths, inclines, structures, types ofsurfaces, traffic, etc.).

Autonomous data machines may observe and analyze their environment basedon a-priori knowledge (e.g., work schedules, license plate database,user databases), sensors on the machines (e.g., LIDAR, camera, infrared,proximity, motion), supporting information (e.g., external databases)and autonomous data machine algorithms. Examples of additional datasources that may be used by the autonomous data machines may includenews, social media, on-site sensor data, user information, local trafficinformation, worker's schedule information, motor vehicle associationinformation, map information, information from law enforcement, localprivate security information, local event information, or any other typeof information. For example, the machines may pull local news and seethat a crime had been committed nearby. The information collectedthrough the machine sensors may be filtered with knowledge of thisnews—for example, an individual matching the description of the suspectof the crime running away quickly may raise an alert. The machines mayperform actions based on the totality of the information, which mayinclude the information from other sources as well as the informationfrom the sensors. This analysis combining different types of informationor information from different sources may occur on-board the autonomousdata machines. The data machines may be able to pull information fromthe various sources in real-time. Alternatively, some or all of theanalysis may occur off-board. For example, the autonomous data machinesmay be capable of communicating with a control center which may performsome or all of the analysis pulling together different sources ofinformation. The control center may then formulate instructions for howthe autonomous data machines should respond.

The autonomous data machines may collect information which may becommunicated to the control center. The control center may aggregate thedata collected from the autonomous data machines. Some of the datacollected from the autonomous data machines may be reflective of thestate or condition of the autonomous data machines. For example, thestate of the autonomous data machines may include machine criticalparameters such as health, location, available range, charging events,and/or abuse. Some of the data collected by the autonomous data machinesmay be reflective of the mission of the autonomous data machines (e.g.,surveillance images if the mission of the autonomous data machine issurveillance). The data from the autonomous data machines may also becombined with data from other sources (e.g., social media, third partydatabases, public information). The information from autonomous datamachines may also be sent to other autonomous data machines or to theuser of the autonomous data machines (e.g., Company A).

Interaction and control of the one or more autonomous data machines mayoccur through the control center. In addition to monitoring the datacollected, the control center may send instructions or information tothe autonomous data machines. In some instances, the instructions may beformulated by the control center in response to data collected from thecontrol center. For example, information about a state of the autonomousdata machines may be used to formulate instructions to the autonomousdata machines in response to the state. For example, if the autonomousdata machine is running low on charge, an instruction may be provided togo ahead and charge at the nearest charging station. If the autonomousdata machine is undergoing abuse, an alert may be provided to a user ofthe autonomous data machine, or an instruction to provide an audiblealarm may be provided.

An autonomous data machine may be able to identify critical events andreact accordingly (e.g., raise an audible alarm, launch a UAV, travel toinvestigate further, retreat to avoid damage) and/or to transmitspecific information to the control center (e.g., high-bandwidth imagedata). The autonomous data machines may take commands from the controlcenter in response to a specific situation or critical event (e.g., “goto location X”, “show information Y”). Alternatively, the autonomousdata machines may be able to formulate their own instructions anddetermine how to respond without aid of the control center. Thus theautonomous data machines may be able to determine how to react on-boardwithout the aid of any external device, or may receive instructions froman external device on how to respond. The instructions from the controlcenter or any other external device may be generated automaticallywithout requiring any human intervention. Alternatively, some humanintervention and/or instruction may be provided. Instructions generatedautomatically (e.g., on-board the machine or externally) may incorporateinformation collected by the sensors of the machine and/or additionaldata sources (e.g., a-priori data sources, external databases, publicinformation, information collected from web-crawling, etc.).Instructions may elicit reactions by the autonomous data machines.Examples of reactions by the data machines may include but are notlimited to: movement, deployment of other autonomous data machines,display of information, audible sounds, collection of specific types ofdata, communication of specific types of data, communication withcertain devices or parties, or charging.

FIG. 11 shows an example of data analytics in accordance with anembodiment of the invention. One or a plurality of autonomous datamachines may be provided. They may collect real-time on-site data usingone or more sensors. In some instances, other on-site data sources maybe provided. For example, fixed cameras, microphones, or other types ofsensors may be provided. The other on-site data sources may be providedin connection with a pre-existing security system. For example, astructure or environment may have security cameras and/or microphonesset up. These may provide additional on-site data in conjunction withthe roaming machines. The on-site data may be communicated to a networkoperation center (NOC), which may also be referred to as a controlcenter.

External data sources may be provided to the NOC. The external data mayinclude existing static data and/or real-time social data. Such externaldata may also be conveyed to the autonomous data machines, indirectlyvia the NOC, or directly.

The NOC may also communicate with one or more customers. The customersmay be individuals or organizations that may be utilizing informationcollected by the NOC. The customers may optionally have one or moreautonomous data machines deployed at the customer sites. Alternatively,they may have access to data collected and/or generated by the NOC.Processed or raw data may be sent from the NOC to the customer. This mayinclude processed or raw data from sensors of the machines, or otheron-site data sources. Event notifications may be communicated betweenthe NOC and the customers. For example, the customers may inform the NOCof certain events and vice versa.

In some instances, the control data may be able to track the state ofmultiple autonomous data machines. For example, multiple autonomous datamachines may be provided within the same geofence. The location of eachof these machines may be tracked. If one of the machines detects aparticular condition, additional machines may be deployed to thatlocation investigate further. In another example while one of themachines is charging, the travel path of another machine may be alteredto accommodate for the machine that is out of play while charging.

This may aid in optimization of utilization, uptime, lifetime, operatingcosts, maintenance costs, of one or more autonomous data machines fromthe control center. The control center may be able to manage a fleet ofthe autonomous data machines to optimize conditions. The provision ofmultiple autonomous data machines may advantageously permit them tointeract or respond to situations in a concerted manner, thus providinga synergistic effect.

The autonomous data system may be used in the planning and execution ofmissions with the one or more autonomous data machines. Missions may beplanned or dictated at the control center. Alternatively, missions maybe planned and/or dictated by a user. The missions parameters may becommunicated to the autonomous data machines or may be entered directlyto the autonomous data machines. The mission may include one or moregoals that may dictate the behavior of the autonomous data machines. Insome embodiments, one or more types of missions may be pre-set.Alternatively, custom missions may be generated. Missions may definegeneral and/or specific behavior of the autonomous data machines. Forexample, the missions may define the type of data collected by theautonomous data machines, the type of paths that the autonomous datamachines will take or types of movement they will make, how theautonomous data machines will interact with humans, the types ofcommunications sent by the autonomous data machines, the alertconditions, charging schedules, or other types of behaviors orreactions.

Some examples of types of missions may include, but are not limited to,surveillance, security, school safety and security monitoring, parkingmonitoring and management, wireless network detection or management,telecommunications device detection or management, guidance and/orinformation, or any other implementations. Examples of types ofimplementations using different missions are described in greater detailelsewhere herein.

Communications

The autonomous data machine may communicate with one or more externaldevices using any communication technique or combinations ofcommunication techniques. The one or more external devices may include acontrol center (e.g., network operation center), other autonomous datamachines, user security center or office, mobile devices of individualsin the area, or any other devices.

In some examples, communication between an autonomous data machine and acontrol center may cover time and location varying availablecommunication bandwidth, e.g., buffering and time-delayed transmission.In some instances, the availability of the bandwidth may be detected andthe data stream may be adjusted in response. For example, if theavailable bandwidth is low, the machine may delay sending large datafiles and may prioritize smaller data files that provide the crucialinformation. For example, rather than sending high resolution images,lower resolution images may be transmitted to the control center.

Communication may occur over multiple different communication channelsto cover varying available communication bandwidth. FIG. 10 shows anexample of different communication channels that may be used. Anautonomous data machine may communicate with a network operation center(NOC). In some instances, it may use a long-range communication, such asa telecommunications network (e.g., 3G/4G cellular communications). Itmay also use short-range communication, such as short range wirelesscommunication (e.g., WiFi). The long-range communication may be analways-on connection with varying QOS. From the NOC, control and missioncommands may be provided. To the NOC, continuous status information maybe provided and reduced-bandwidth sensor feed. The short-rangecommunication may be a temporary high-bandwidth connection at intervalsalong the path. To the NOC, high-quality sensor readout from temporarybuffer may be provided.

In some instances, the long-range communication may include one or morerelay stations (e.g., ground stations, towers, satellites) between theautonomous data machine and the NOC. In some instances, the short-rangecommunication may be direct communication between the autonomous datamachine and the NOC, or point-to-point communication.

Thus, in some instances, a lower bandwidth connection may becontinuously and reliably provided. This may permit the machine tofunction and send some information back to the NOC. However, in someinstances, it may be desirable to send larger quantities of data, whichmay require a higher bandwidth. A second higher bandwidth connection maybe temporarily established in order to permit the transmission of thegreater amount of data (e.g., high definition image files). The higherbandwidth connection may be established in response to a detectedcondition by the machine. The machine may initiate the higher bandwidthconnection. In other instances, the NOC may review information passed bythe machine on the lower bandwidth connection and detect a conditionwhere it may be desirable to pull more information. The NOC mayoptionally initiate the higher bandwidth connection. The higherbandwidth connection may occur periodically in accordance with apre-determined frequency or schedule, and/or may occur in response to adetected event. The higher bandwidth may be provided concurrently withthe lower-bandwidth connection, or may be provided as an alternative tothe lower-bandwidth connection. Information may be passed simultaneouslyalong both bandwidths, or may only occur through a single selectedbandwidth at a time.

In some instances, the communications may pass through the cloud. Inother instances, direct communications may be employed. In someinstances, the NOC may have an Ethernet connection to the cloud. Anyother type of connection may be employed.

The data streams from or to the autonomous data machines may becompressed. In some embodiments, compression of the data streams in theautonomous data machines may limit bandwidth requirements based onavailable bandwidth, required data fidelity and relevance of containedinformation. In some instances, compression may occur by discardingstatic information, and/or permitting transmission of data only whenchanges occur or only when a critical situation is detected.Accordingly, compression may occur to fit the available bandwidth. Asshown in FIG. 10, if the data does not fit the available bandwidth,additional channels of communication may occur.

Digital watermarking may occur between communications between theautonomous data machine and the NOC. The digital watermark may beinformation covertly hidden in the carrier signal. The digitalwatermarking may be used to verify the authenticity or integrity of thecarrier signal or to show the identity of its owners. Digitalwatermarking may be provided to ensure legal compliance. In someinstances, the digital watermarking can be used for tracing copyrightinfringement of the transmitted media.

In some embodiments, data transmission may occur while the autonomousdata machine is charging. When the autonomous data machine is chargingit may need to temporarily stay at a predetermined location.Transmission of large data (e.g., high resolution video data) that isnot immediately time critical may preferably occur during charging. Insome instances, some of the data collected by the machine sensors may bestored at the machine and not immediately transmitted. The storedinformation may then be transmitted while the machine is charging or atother convenient times. The stored information may be transmitted when ahigher bandwidth channel is opened up.

Charging

The autonomous data machines may have a rechargeable power supply, suchas rechargeable batteries. The batteries may permit the autonomous datamachines to operate without charge for an extended period of time. Forexample, from a full charge, the autonomous data machines may be capableof operating at least 1 hour or more, 2 hours or more, 3 hours or more,4 hours or more, 5 hours or more, 6 hours or more, 8 hours or more, 10hours or more, 12 hours or more, 16 hours or more, 20 hours or more, 24hours or more, 36 hours or more, or 48 hours or more. In some instances,the maximum amount of time that the autonomous data machine may becapable of operating without charge may be about 8 hours, 10 hours, 12hours, 16 hours, 20 hours, 24 hours, 36 hours, 48 hours, or 72 hours.

While the autonomous data machine is not charging, it may be capable ofroaming about its environment. In some instances, it may have apreplanned path. For example, the machine may cycle through one or morepredetermined routes repetitively. It may alter its route if it detectsa condition of interest. For example, if it detects an event occurringor interacts with a human, it may alter its route. Alternatively, it mayrandomly wander within certain parameters. A path may be randomlygenerated (e.g., randomly generated ahead of time or made-up as themachine goes). The autonomous data machine may also be capable ofcollecting data (e.g., images). An autonomous data machine's path may bealtered based on the collected data. In some instances, the roaming mayoccur freely in any direction in the environment. In other instances,the roaming may be limited by certain parameters within the environment.For instance, the machine may be limited to a certain type of surface(e.g., paved surface). The machine's path may or may not depend on thelocation of other machines in the environment. For example, a user mayspecify that under normal conditions it may be desirable to spread outthe machines within the environment, so paths may be generated to avoiddense clustering and provide a more even distribution of machines.

In some instances, the autonomous data machine may operate at differentpower consumption levels. For example, the autonomous data machine mayhave a default state where it may effect some movement and some datacollection. However, when it detects particular conditions, a higherpower consumption state may be provided. For example, if it detects aninteresting event occurring, it may move toward the event to investigatefurther and employ continuous use of additional sensors, which may usemore power. It may also communicate more actively with external devices,such as the control center, which also use more power. In someinstances, the autonomous data machine may be at a ‘resting’ or‘sleeping’ state where it is performing minimal movement or datacollection. However, the autonomous data machine may be roused from itsresting or sleeping state when it detects certain conditions.

The power supplies of the autonomous data machines may be rechargedusing any technique. For example, one or more inductive charging matsmay be employed. The charging mat may be a wired or wireless mat. FIGS.12A and 12B shows examples of inductive charging mechanisms inaccordance with an embodiment of the invention. A mat 1210 may underliea machine. Another inductive charger 1220 may be provided over which themachine may drive. The inductive chargers may be located on a ground andthe autonomous data machines may drive over the inductive chargers. Inanother example, the inductive chargers may be provided on a wall, andthe inductive chargers may drive over to the wall to charge. Theinductive charging mechanisms may permit the autonomous data machines torecharge without having to plug-in or create a mechanical connection. Inalternative embodiments of the invention, plugs or mechanicalconnections may be used.

In some instances, the amount of time the autonomous data machines spendcharging may be less than 1%, 3%, 5%, 7%, 10%, 15%, 20%, 25%, or 30% ofthe amount of time it spends in operation. For example, a 5 minutecharge may be sufficient to keep the autonomous data machine operationalfor more than 15 minutes, 20 minutes, 30 minutes, 45 minutes, an hour,or two hours. In another example, an hour charge may be sufficient tokeep the autonomous data machine operational for more than four hours,six hours, eight hours, ten hours, 12 hours, or fifteen hours.

In some embodiments, the autonomous data machines may determine anoptimum charging schedule. The optimum charging schedule may depend onthe mission of the autonomous data machines, availability of otherautonomous data machines, placement of charging stations, or any otherfactors. For example, if many charging stations are provided atdifferent points throughout the location, the autonomous data machinesmay charge for shorter periods of time, because the likelihood that acharging station will be nearby and available may be increased. Bycontrast if fewer charge stations are provided and are only at aparticular location, the autonomous data machines may charge for longersince they will likely be traveling a longer path during which fewercharging stations may be available. The length of charge may depend onavailability of other machines to take the charging machine's place. Forexample, if a particular region is to undergo continuous surveillance,an autonomous data machine may charge for a longer period of time ifother machines are available to patrol that particular region. If fewermachines are available, the autonomous data may charge for a shorterperiod of time, to patrol the region until other machines can come overand take its place.

The autonomous data machines may remain powered on while charging. Forexample, while an autonomous data machine is sitting on an inductivecharging mat, it may still perform data collection, analysis and/orcommunication functions. The controller may also remain in operation.This may provide maximum uptime for the autonomous data machine.

In one example, while the autonomous data machine is charging it may begenerating a surveillance path. For example, generation of a randomsurveillance path and timing may be provided in conjunction with vehiclecharging. Generation of the random surveillance path may take intoaccount a future charge time and/or location. In some examples, thesurveillance paths of the machines may vary. New paths may be generatedwhile the machines are charging. These new paths may take into accountmission parameters, locations of other machines, and/or chargingrequirements and/or availability of charging stations.

An additional example may provide that data transmission may occur whilethe autonomous data machine is charging. When the autonomous datamachine is charging it may need to temporarily stay at a predeterminedlocation. Transmission of large data that is not immediately timecritical may preferably occur during charging. Data transmission mayalso use charging power connection as an additional data channel, suchas by modulating information on top of a charging energy stream totransmit data.

In another example, while the autonomous data machine is charging,additional surveillance may occur with aid of a smaller autonomous datamachine. For instance, air surveillance may occur while charging. A UAV(e.g., tethered UAV) may be used which may obtain power from theautonomous data machine or directly from the charging station. The UAVmay input data to the machine while it is autonomously charging on thecharging mat, and download information. This may protect the autonomousdata machine while charging and still maintain surveillance capabilityfrom above. For example, the UAV may capture images of the autonomousdata machine and surrounding environment. If the UAV detects a conditionthat may cause alarm, it may send the information to the autonomous datamachine to stop charging and react.

Service/Abuse

Autonomous data machines may have self-protection or preservationprotocols. In one example, autonomous data machines may authenticateindividuals before providing the individuals with access to theautonomous data machines. In some instances, only certain privilegedpersonnel may be authorized to access the machines. The access may beprovided for service, repairs, maintenance, control, or data access.

The authentication of the individual may occur via one or moreinteraction channels, such as wireless, IR, or visible light signal,visual pattern (e.g., similar to QR code), RFID, or otherelectromagnetic systems. For example, the individuals may have one ormore device that may emit a signal detectable by the machines, and thatmay authenticate the individuals. In another example, the individualsmay speak a password or phrase that may be recognized by the machines.Similarly, an image may be captured of the individuals, and if theirface or appearance matches that of pre-approved personnel they may beprovided access. Similarly, a fingerprint, handprint, retinal scan, orother biometric data may be used to authenticate the individual andprovide them with access to the machine.

If an individual is not authenticated or recognized as privilegedpersonnel, they may not be granted access to the machines. For example,the machines may not open an access port or panel to provide theindividual access to the relevant portions of the machine. In anotherexample, the machines may retreat from the individual if they failauthentication. Furthermore, the machines may issue an audible alarm orsend an alert to an external device or user if the individual persistsin trying to gain access.

The autonomous data machines may resist abuse. For example, they mayhave a spray-resistant coating that may prefer vandals from spraypainting the machines. They may also have a robust housing that may beresistant to water or dents. For example, even if the machine is hit bya stick or other item, the housing may protect the autonomous datamachines.

The autonomous data machines may be capable of detecting and reacting tophysical abuse. Examples of physical abuse may include, but are notlimited to, tipping, carrying away, moving away on trailer, tampering,hitting, or similar actions. Detection may occur using location,position, motion, and other sensors such as the IMU, GPS, a wheelencoder, LIDAR, camera, or microphone.

If an attack on the autonomous data machine is detected, it may providean audible alarm (e.g., similar to a car alarm). It may also provide avisual display to attract attention (e.g., flashing of bright lights).The autonomous data machine may record a user abusing the data machineand store and/or transmit the recording. The autonomous data machine mayspray a permanent dye, pepper spray, or other substance on a human thatabuses that autonomous data machine. The autonomous data machine maydeliver a mild electric shock to a human that abuses the autonomous datamachine. The autonomous data machine may retreat from the human. It mayalso send alerts to one or more external device. For example, it maysend information to a local law enforcement, private security, or othersecurity station (e.g., “call for help”). It may also informationadditional autonomous data machines. For example, if other machines arein the proximity, they may come closer and provide their own alarms andcapture additional images of the situation. The machine may alsorecording and communication of the situation.

Autonomous data machines may be able to sense conditions of the machineusing one or more sensor. In some instances, the machines may be able tosense a condition where there may be damage to the machine, regardlessof whether a human was involved. For example, a temperature sensor maypick up an unusual amount of heat within the machine. The machine may ormay not try to self-diagnose the problem. An alert may be provided to anauthorized individual to investigate the problem further. The machinemay or may not take action in response to certain problems. For example,if unusual overheating is occurring it may shut down part or all of itsfunctions.

In some instances, the autonomous data machines may be semi-autonomous.Control of the movement of the autonomous data machines may occurutilizing gestures, such as hand gestures or body position (e.g., usinga Leap Motion type sensor). In some instances, this gesture basedcontrol may only operate for individuals who are recognized asprivileged personnel. Alternatively, gesture based control may alsooccur for any other individual regardless of whether they areprivileged. Other types of speech-based control may also be employed orrecognized. In some instances, the responsiveness of the machine to anindividual may depend on the type of command. For example, onlyprivileged personnel may be obeyed for a command to open up a controlpanel to provide access to the interior of the machine, while anyone maybe obeyed for a command to stop moving.

The autonomous data machines may be capable of detecting and reacting tohacking. For example, analysis of usage and communication patterns mayoccur. Also, this may occur by matching location of wirelesscommunication with known authenticated locations and media, such as WiFiaccess points and cell phone towers. If commands are detected as comingfrom unauthorized sources, authorized sources may be alerted and/or themachines may ignore the ‘hacked’ commands until they are verified.

Implementations

Autonomous data machines may be used in various implementations or undercombinations of various implementations. The autonomous data systems,which may include the control center may also be used in the selectedimplementations.

One or more mission may be provided for the machines. The missions maydefine the implementation of the machines, and the types of actions thatwill be taken by the machines. The missions may dictate how the machinesmove and react to certain situations. The missions may also dictate thetype of information that is collected and transmitted to a controlcenter, and conditions under which certain communications may occur. Thefollowing implementations are provided by way of example only. In someinstances, autonomous data machines may arrive pre-loaded with missions.Alternatively, they may be given new missions, or missions may bere-assigned or redefined. In some instances, software may be downloadedto the machines to determine the missions and/or provide later versionsor implementations of the missions. Missions may be provided and/orcontrolled by the control center. Alternatively, missions may beprovided and/or controlled by a user (e.g., Company A that may be usingthe machines to monitor Company A's environment). In some instances,missions may be selected from a predetermined set of missions.Alternatively, custom missions may be created. Missions may set generalparameters and/or specific parameters in how the machines will operate.

Security applications may be provided for autonomous data machines. Themission of the autonomous data machines may be to increase security of alocation. The autonomous data machines may provide security at a definedlocation or within a particular environment. For example, the machinesmay perform perimeter surveillance of environments such as a datacenter, airport, seaport, ground transportation center, home,subdivision, neighborhood or community, mall, art gallery, corporatecampus, school, traffic hub, construction site, hospital, militaryinstallation, sporting event, concert, live performance, warehouse, orany other location or environment. FIG. 13 provides an example of anautonomous data machine patrolling a shopping center. The machines maybe capable of detecting movement of physical objects using one or moresensors. The detected movement may be recorded with a time and locationstamp. For example, the machines may detect the presence of one or morehumans in the vicinity. In some instances, the humans may be authorizedor not authorized to be at the location. The machines may be able toidentify whether the detected humans are authorized to be at thelocation. For example, during business hours the public may beauthorized to be at a location, while after business hours, onlysecurity staff may be authorized to be at the location. The machines mayalso be capable of detecting before/after situations. For example, themachines may visit a location at a first time, and then visit the samelocation again later at a second time. The machines may be able toquickly identify differences at that location between the first andsecond time. For example, the machine may see that an item is missing orout of place.

In some embodiments, the presence of the autonomous data machines mayaid in security, even without performing any actions. For example, thephysical presence of the machines may have a crime deterrent effect. Thephysical presence/appearance may be optimized to maximize the crimedeterrence effect. For example, if a potential criminal knows that themachines are around and are capable of capturing potentiallyincriminating data and/or raising alarms, the criminal may avoid thearea at which the machines are present. Thus, the mere presence of theautonomous data machines may reduce the rate of crime at a locationwhere they are deployed.

Security applications may cause the autonomous data machines to utilizeaudio analytics. Detection and analysis of background and environmentalnoise for the purposes of monitoring and security may include analysisof footsteps, raindrops, car alarms, home burglar alarms, smartphones,voices, vehicle exhaust, gunshots, leaves, wind, garbage trucks, airconditioning units, fire alarms, tires squealing, car horns, vehicleengines, police sirens, ambulance sirens, fire truck sirens, papersshuffling, car door closing, screaming, music or other types of sound.The audio analysis may be able to separate background noises fromsignificant noises. The significant noises may be analyzed to determinewhether an alert condition is provided. For example, the sound ofscreaming or gunshots may be reason for the autonomous data machines toprovide an alert to law enforcement or a private security, while normalconversational voices are not.

Detection and analysis of background and environmental noise may also beperformed. This may occur for the purposes of navigation in complexenvironments. For example, identification of a car honking whenmonitoring a parking lot may identify and localize a potentialnavigation conflict with one or more cars, such as backing in and out ofparking lots within the motion of the autonomous data machines.

The system may provide alarm integration. For example, in a securityimplementation, one or more autonomous data machines may be coupled toan existing security system. For example, if the autonomous data machineis patrolling outside a home, it may be couple to the home's existingburglar alarm system. In another example, if the autonomous data machineis patrolling outside a warehouse or corporate campus, it may beconnected to that warehouse or corporate campus' security system. If theenvironment that the autonomous data machine is monitoring has apre-existing security system, the autonomous data machine may becommunicating with the system. In some instances, information from theautonomous data machine and system may be compiled at a control center.

Use autonomous data machines may provide home alarm system improvement.For instance, a large number of home alarms that go off are falsealerts. An autonomous data machine may be used to validate an alarm thathas gone off. The machine may use its sensing capabilities to confirmthat a home alarm is more valid.

Crowdsourcing of security may occur. For example, a device may beprovided for a community to engage, report, and interact through anumber of means (e.g., text, video, audio, email, posts) in order tohelp address criminal activity. The systems and methods described hereinmay incorporate data gathered through individuals in the environment.For examples individuals may post information online or send informationto a control center. In some instances, individuals may utilize mobiledevices (e.g., smartphones, tablets) to capture images and/or provideinformation. Such information may be incorporated with data gatheredfrom autonomous data machines. In some instances, information posted byother individuals may be sent to the autonomous data machines or mayaffect operation of the autonomous data machines. In some instances,information gathered by machines may be sent to local mobile devices, orthe control center may analyze data from the machines in combinationwith other data and send alerts to local mobile devices.

In one example, the system may be used to detect weapons of massdestruction (WMD). The detection of WMD may occur using an autonomousdata machine. A WMD detection module may be an optional payload of themachine. For example one or more chemical sensors, biological sensors,radiation sensors, and/or pathogen sensors may be provided. If a WMD isprovided an alert may be provided to the appropriate law enforcementagency and/or private security. In some instances, an audible alarm maynot be provided to not raise the suspicions of the party with the WMDand cause the party to set off the alarm. In other implementations, analarm may be provided to the surrounding individuals to vacate the areaas soon as possible. This may be preferable in situations where nohumans are found to be holding or controlling the WMD.

In another example, the system may be used for security at a school. Fora school pairing implementation, the autonomous data machine may bedeployed at a school. It may match a child's face, using a facialrecognition program, to a license plate with optical characterrecognition. The machine may use this information to make sure thecorrect child is getting into a vehicle, or that a child is departing inthe correct vehicle. For example, the children of the school may bepre-registered, along with permitted vehicles. The image sensor of themachine may capture an image of the child and the vehicle and/or licenseplate. A comparison may be made (e.g., on-board the machine or at acontrol center) to determine whether the vehicle is a permitted vehiclethat is pre-registered for the child. In some embodiments, if it is nota permitted vehicle, the machine may send an alert to the school or aparent of the child. The machine may send a query to the parent of thechild or other authorized individual to authorize the vehicle. If thevehicle is authorized, no further action may be taken. If the vehicle isnot authorized, further alerts may be provided (e.g., to lawenforcement) or audible alarms may be provided. Images of the vehicleand/or drive may be captured and/or communicated.

Autonomous data machines may be useful for detection and tracking ofindividual objects as shown in FIG. 6D. In some instances, a pluralityof autonomous data machines may be distributed over an area. When anobject, such as a human being, is being tracked, images collected by themultiple data machines may be compared and analyzed. For example, asuspect may be fleeing the scene of a crime. The image of the suspectmay be captured by multiple machines the movement of the suspect may betracked by where and when the machines capture the image of the suspect.Facial and/or object recognition may be useful to aid in the tracking.In another example, law enforcement may wish to track the movement of anobject, such as a suitcase. Even if the suitcase is handed off todifferent people, object recognition used by the multiple data machinesmay aid in keeping track of the location of the suitcase.

In some embodiments, the autonomous data machines may be used forgreeting and/or guidance. The mission of the autonomous data machinesmay be to welcome humans to a location and help them with any needs. Forexample, machines may be deployed at a corporate headquarters, collegecampus, or museum to aid in directing visitors or providing usefulinformation. The machines may make informative announcements such astime, news, upcoming events, or any information relevant to theirlocation.

In additional implementations, the autonomous data machines may be usedto regulate parking. A mission of the autonomous data machine may be tofacilitate parking at a parking facility and/or track activity at theparking facility. A security element may also be provided, where theautonomous data machine may have a mission to minimize accidents orcrime at the parking facility. One or more autonomous data machines maybe deployed at car parking facilities. License plates may be detected.For example, image capture devices of the machines may be used to takesnapshots of the license plates. Optical character recognition maypermit the machines to ‘read’ the license plates. Mobile automatedlicense plate recognition may permit the monitoring of license platesutilizing a machine in a certain predetermined range of speed (e.g.,about 1 mph-25 mph). FIG. 6A provides an example of license platerecognition using one or more vision sensor of the autonomous datamachines. Detection of the license plates may be connected with locationand time. Authentication and logging against a database may occur. Forexample, the information about the license plates may be run against anexternal data base, such as a motor vehicle association database. Imagesof the vehicle on which the license plates are mounted may also becollected and/or analyzed to determine whether they match theinformation in the databases. If a stolen license plate/vehicle isdetected the machines may make note and/or update databases or providealerts.

The machines may also be used to detect free parking spots. Their usagemay develop parking use patterns to optimize and predict parking spaceutilization. In some instances, machines at different locations at aparking structure may communicate with one another about detectedparking spaces. This information may be used to direct vehicles to openspots or areas of the parking structure with many more openings. A humanmachine interface for parking facility operators and users may beprovided, such as mobile applications or web sites.

In additional implementations, the machines may be used overseeconstructions sites. A mission of a machine may be to oversee progressand safety at the construction sites. FIG. 6B shows an example ofprogress mapping at a construction site. The autonomous data machinesmay be capable of detecting before and after conditions. For example,the autonomous data machine may collect an image of a site at a firsttime. Then the autonomous data machine may collect an image of the siteat a second time. The images may be compared to see what changes weremade. In some instances, the changes may indicate progress, and mayhighlight what progress has been established.

Machines may also be used to view and/or analyze traffic. This mayinclude pedestrian traffic, as shown in FIG. 6C, vehicle traffic, airtraffic, or any other type of traffic. This information may be providedto a control center and may be useful in various applications.

Autonomous data machines may also be used to detect networks. A machinemay detect WiFi and other wireless networks. Network maps may be createdand shared by the machine and/or control center. In some instances“rogue networks” may be detected and analyzed with aid of the machine.The roaming nature of the machine may be useful for detecting signalsand creating maps of items that may not be readily visually discernible.

Similarly, autonomous data machines may be useful for detecting andlocalizing cell phones using their specific signature with wirelesssystems, such as via Bluetooth or WiFi identifier. Telecommunicationnetworks may thus also be detected and/or mapped.

In additional implementations, the autonomous data machines may carryadditional user-provided sensors as black box sensors. User providedsensors and data processing may generate specific missions and requestsfor autonomous data machines that may be fed back to the autonomous datamachine with a defined priority. This may permit a user to apply blackbox applications outside of the system and oversight (e.g., controlcenter system and oversight). The user may specify and design themissions for the machine. For example, the user may be an industryfacility, and may create a proprietary identification system in theindustry facility.

Autonomous data machines may be capable of performing mixed applicationsin an environment, such as surveillance, access control, parametermonitoring, identification and tracking of objects and people,identification of damages or other physical changes. In another example,the machines may be capable of a combination of security implementationsand parking structure management. In another example, the machines mayperform a combination of network mapping as well as crime mapping.

It should be understood from the foregoing that, while particularimplementations have been illustrated and described, variousmodifications can be made thereto and are contemplated herein. It isalso not intended that the invention be limited by the specific examplesprovided within the specification. While the invention has beendescribed with reference to the aforementioned specification, thedescriptions and illustrations of the preferable embodiments herein arenot meant to be construed in a limiting sense. Furthermore, it shall beunderstood that all aspects of the invention are not limited to thespecific depictions, configurations or relative proportions set forthherein which depend upon a variety of conditions and variables. Variousmodifications in form and detail of the embodiments of the inventionwill be apparent to a person skilled in the art. It is thereforecontemplated that the invention shall also cover any such modifications,variations and equivalents.

What is claimed is:
 1. A system for controlling one or more autonomousdata machines configured to perform surveillance and monitor securitywithin a predefined location, the system comprising: a control center inremote communication with the one or more one or more autonomous datamachines, wherein the control center comprises one or more processorslocated remotely from the one or more autonomous data machines and areindividually or collectively configured to: receive data indicative ofat least one security event occurring at the predefined location,wherein at least a portion of the data is from a plurality of sensorson-board the one or more autonomous data machines, wherein the datacomprises location of a mobile device detected by at least one of thesensors configured to detect a wireless network signal of the mobiledevice that is not directed to the one or more autonomous data machines;analyze the data to generate a set of instructions for responding to theat least one security event; and provide the set of instructions to theone or more autonomous data machines, wherein one or more processors onthe one or more autonomous data machines are individually orcollectively configured to execute the set of instructions, therebycausing the one or more autonomous data machines to perform an action inresponse to the at least one security event.
 2. The system of claim 1,wherein the wireless network signal is used to create a map of itemsthat may not be visually discernable.
 3. The system of claim 2, whereinthe map comprises a network map.
 4. The system of claim 1, wherein thelocation of the mobile device is determined using a specific signatureof the wireless signature.
 5. The system of claim 1, wherein at least aportion of the data comprises information obtained from an online datasource external to the one or more autonomous data machines.
 6. Thesystem of claim 5, wherein the data indicative of at least one securityevent occurring in the predefined location is updated substantially inreal-time as the data is being collected using the plurality of sensorsand/or obtained from the online data source.
 7. The system of claim 1,wherein the security event occurs in the predefined location external tothe one or more autonomous data machines and comprises at least one ofthe following: (i) a crime, (ii) a potential criminal activity, (iii) anaccident, or (iv) an emergency.
 8. The system of claim 1, wherein thedata is further indicative of an operational state of the one or moreautonomous data machines, wherein the operational state of the one ormore autonomous data machines is associated with at least one of thefollowing: (i) an operational health, (ii) a location, (iii) a range oftravel, (iv) a state of charge, or (v) a state of abuse, of the one ormore autonomous data machines.
 9. The system of claim 1, wherein the oneor more processors in the control center are configured to generate andprovide the set of instructions to the one or more autonomous datamachines without any human intervention.
 10. The system of claim 9,wherein the one or more autonomous data machines are configured tolocate and move to a nearest charging station for charging based on theset of instructions when the operational state is indicative of a lowstate of charge.
 11. The system of claim 1, wherein the set ofinstructions are executed to: (i) navigation of the one or moreautonomous data machines to a location where the security event isoccurring and (ii) at least one of the following: transmission ofinformation about the security event, generation of audible sounds,collection of a predefined data type, or communication of the predefineddata type to one or more other devices or third parties.
 12. The systemof claim 1, wherein the one or more autonomous data machines areconfigured to transmit the data collected by the plurality of sensors tothe system according to a pre-determined schedule.
 13. The system ofclaim 1, wherein the plurality of sensors comprise (i) an image sensorand (ii) at least one of the following: an audio sensor, a thermalsensor, an infrared sensor, a proximity sensor, a motion sensor, or aposition sensor.
 14. The system of claim 1, wherein the data is alsocollected using at least one sensor located onsite within the predefinedlocation, and wherein the at least one sensor is remote from the one ormore autonomous data machines.
 15. The system of claim 1, wherein thedata indicative of at least one security event occurring in thepredefined location comprises data obtained from (i) the a plurality ofsensors on-board the one or more autonomous data machines and (ii) atleast one of the following: a-priori knowledge and public data sources.16. The system of claim 1, wherein the predefined location is defined byone or more geofences, and wherein the one or more autonomous datamachines are configured to operate within the one or more geofences. 17.The system of claim 1, wherein the set of instructions are executed bythe one or more autonomous data machines to cause at least one of theautonomous data machines to alter its travel path to accommodate atleast another autonomous data machine that is in charging.
 18. A methodfor controlling one or more autonomous data machines configured toperform surveillance and monitor security within a predefined location,the method comprising: with aid of one or more processors locatedremotely from the one or more autonomous data machines individually orcollectively: receiving data indicative of at least one security eventoccurring in the predefined location, wherein at least a portion of thedata is from a plurality of sensors on-board the one or more autonomousdata machines, wherein the data comprises location of a mobile devicedetected by at least one of the sensors configured to detect a wirelessnetwork signal of the mobile device that is not directed to the one ormore autonomous data machines; analyzing the data to generate a set ofinstructions for responding to the at least one security event; andproviding the set of instructions to the one or more autonomous datamachines, wherein a plurality of processors on the one or moreautonomous data machines are individually or collectively configured toexecute the set of instructions, thereby causing the one or moreautonomous data machines to perform an action in response to the atleast one security event.
 19. A non-transitory computer readable mediumstoring instructions that, when executed by one or more processorslocated remotely from the one or more autonomous data machines, causesthe one or more processors to individually or collectively perform amethod for controlling one or more autonomous data machines configuredto perform surveillance and monitor security within a predefinedlocation, the method comprising: receiving data indicative of at leastone security event occurring at the predefined location, wherein atleast a portion of the data is from a plurality of sensors on-board theone or more autonomous data machines, wherein the data compriseslocation of a mobile device detected by at least one of the sensorsconfigured to detect a wireless network signal of the mobile device thatis not directed to the one or more autonomous data machines; analyzingthe data to generate a set of instructions for responding to the atleast one security event; and providing the set of instructions to theone or more autonomous data machines, wherein a plurality of processorson the one or more autonomous data machines are individually orcollectively configured to execute the set of instructions, therebycausing the one or more autonomous data machines to perform an action inresponse to the at least one security event.
 20. The method of claim 19,wherein the wireless network signal is used to create a map of itemsthat may not be visually discernable.